{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cointegration Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:21.527344Z",
     "start_time": "2020-06-19T02:31:21.522989Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:22.262759Z",
     "start_time": "2020-06-19T02:31:21.682771Z"
    }
   },
   "outputs": [],
   "source": [
    "from time import time\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.tree import  DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "import numpy as np\n",
    "from numpy.linalg import LinAlgError\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller, coint\n",
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "from statsmodels.tsa.api import VAR\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:22.984487Z",
     "start_time": "2020-06-19T02:31:22.978264Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: f'{x:,.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:23.301264Z",
     "start_time": "2020-06-19T02:31:23.298171Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_PATH = Path('..', 'data')\n",
    "STORE = DATA_PATH / 'assets.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Johansen Test Critical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:25.053287Z",
     "start_time": "2020-06-19T02:31:25.043062Z"
    }
   },
   "outputs": [],
   "source": [
    "critical_values = {0: {.9: 13.4294, .95: 15.4943, .99: 19.9349},\n",
    "                   1: {.9: 2.7055, .95: 3.8415, .99: 6.6349}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:25.243995Z",
     "start_time": "2020-06-19T02:31:25.238882Z"
    }
   },
   "outputs": [],
   "source": [
    "trace0_cv = critical_values[0][.95] # critical value for 0 cointegration relationships\n",
    "trace1_cv = critical_values[1][.95] # critical value for 1 cointegration relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load & Clean Stock & ETF Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove highly correlated assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:27.271553Z",
     "start_time": "2020-06-19T02:31:27.248596Z"
    }
   },
   "outputs": [],
   "source": [
    "def remove_correlated_assets(df, cutoff=.99):\n",
    "    corr = df.corr().stack()\n",
    "    corr = corr[corr < 1]\n",
    "    to_check = corr[corr.abs() > cutoff].index\n",
    "    keep, drop = set(), set()\n",
    "    for s1, s2 in to_check:\n",
    "        if s1 not in keep:\n",
    "            if s2 not in keep:\n",
    "                keep.add(s1)\n",
    "                drop.add(s2)\n",
    "            else:\n",
    "                drop.add(s1)\n",
    "        else:\n",
    "            keep.discard(s2)\n",
    "            drop.add(s2)\n",
    "    return df.drop(drop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove stationary series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:27.550333Z",
     "start_time": "2020-06-19T02:31:27.540604Z"
    }
   },
   "outputs": [],
   "source": [
    "def check_stationarity(df):\n",
    "    results = []\n",
    "    for ticker, prices in df.items():\n",
    "        results.append([ticker, adfuller(prices, regression='ct')[1]])\n",
    "    return pd.DataFrame(results, columns=['ticker', 'adf']).sort_values('adf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:29.502729Z",
     "start_time": "2020-06-19T02:31:29.495729Z"
    }
   },
   "outputs": [],
   "source": [
    "def remove_stationary_assets(df, pval=.05):\n",
    "    test_result = check_stationarity(df)\n",
    "    stationary = test_result.loc[test_result.adf <= pval, 'ticker'].tolist()\n",
    "    return df.drop(stationary, axis=1).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:31:29.909748Z",
     "start_time": "2020-06-19T02:31:29.896367Z"
    }
   },
   "outputs": [],
   "source": [
    "def select_assets(asset_class='stocks', n=500, start=2010, end=2019):\n",
    "    idx = pd.IndexSlice\n",
    "    with pd.HDFStore(STORE) as store:\n",
    "        df = (pd.concat([\n",
    "            store[f'stooq/us/nasdaq/{asset_class}/prices'],\n",
    "            store[f'stooq/us/nyse/{asset_class}/prices']\n",
    "        ]).sort_index().loc[idx[:, str(start):str(end)], :])\n",
    "        df = df.reset_index().drop_duplicates().set_index(['ticker', 'date'])\n",
    "        df['dv'] = df.close.mul(df.volume)\n",
    "        dv = df.groupby(level='ticker').dv.mean().nlargest(n=n).index\n",
    "        df = (df.loc[idx[dv, :],\n",
    "                     'close'].unstack('ticker').ffill(limit=5).dropna(axis=1))\n",
    "\n",
    "    df = remove_correlated_assets(df)\n",
    "    return remove_stationary_assets(df).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:14.228319Z",
     "start_time": "2020-06-19T02:31:30.609085Z"
    }
   },
   "outputs": [],
   "source": [
    "for asset_class, n in [('etfs', 500), ('stocks', 250)]:\n",
    "    df = select_assets(asset_class=asset_class, n=n)\n",
    "    df.to_hdf('data.h5', f'{asset_class}/close')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get ticker dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:25.743705Z",
     "start_time": "2020-06-19T02:32:25.739044Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_ticker_dict():\n",
    "    with pd.HDFStore(STORE) as store:\n",
    "        return (pd.concat([\n",
    "            store['stooq/us/nyse/stocks/tickers'],\n",
    "            store['stooq/us/nyse/etfs/tickers'],\n",
    "            store['stooq/us/nasdaq/etfs/tickers'],\n",
    "            store['stooq/us/nasdaq/stocks/tickers']\n",
    "        ]).drop_duplicates().set_index('ticker').squeeze().to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:25.792751Z",
     "start_time": "2020-06-19T02:32:25.745584Z"
    }
   },
   "outputs": [],
   "source": [
    "names = get_ticker_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Correlation Clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:25.870237Z",
     "start_time": "2020-06-19T02:32:25.794039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 172 entries, AA.US to YUM.US\n",
      "dtypes: float64(172)\n",
      "memory usage: 3.3 MB\n"
     ]
    }
   ],
   "source": [
    "stocks = pd.read_hdf('data.h5', 'stocks/close')\n",
    "stocks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:25.882602Z",
     "start_time": "2020-06-19T02:32:25.871321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 139 entries, AAXJ.US to YCS.US\n",
      "dtypes: float64(139)\n",
      "memory usage: 2.7 MB\n"
     ]
    }
   ],
   "source": [
    "etfs = pd.read_hdf('data.h5', 'etfs/close')\n",
    "etfs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:26.911466Z",
     "start_time": "2020-06-19T02:32:25.883450Z"
    }
   },
   "outputs": [],
   "source": [
    "tickers = {k: v for k, v in names.items() if k in etfs.columns.union(stocks.columns)}\n",
    "pd.Series(tickers).to_hdf('data.h5', 'tickers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.202889Z",
     "start_time": "2020-06-19T02:32:26.912320Z"
    }
   },
   "outputs": [],
   "source": [
    "corr = pd.DataFrame(index=stocks.columns)\n",
    "for etf, data in etfs.items():\n",
    "    corr[etf] = stocks.corrwith(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.211251Z",
     "start_time": "2020-06-19T02:32:31.204091Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 172 entries, AA.US to YUM.US\n",
      "Columns: 139 entries, AAXJ.US to YCS.US\n",
      "dtypes: float64(139)\n",
      "memory usage: 193.1+ KB\n"
     ]
    }
   ],
   "source": [
    "corr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.735649Z",
     "start_time": "2020-06-19T02:32:31.212390Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.clustermap(corr, cmap=cmap, center=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Candidate Selection using Heuristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computational Complexity: Comparing running times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.739098Z",
     "start_time": "2020-06-19T02:32:31.736517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2516, 172), (2516, 139))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.shape, etfs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.754735Z",
     "start_time": "2020-06-19T02:32:31.739865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 172 entries, AA.US to YUM.US\n",
      "dtypes: float64(172)\n",
      "memory usage: 3.3 MB\n"
     ]
    }
   ],
   "source": [
    "stocks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.764893Z",
     "start_time": "2020-06-19T02:32:31.755629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 139 entries, AAXJ.US to YCS.US\n",
      "dtypes: float64(139)\n",
      "memory usage: 2.7 MB\n"
     ]
    }
   ],
   "source": [
    "etfs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.775722Z",
     "start_time": "2020-06-19T02:32:31.765957Z"
    }
   },
   "outputs": [],
   "source": [
    "security = etfs['AAXJ.US'].loc['2010': '2013']\n",
    "candidates = stocks.loc['2010': '2013']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.816193Z",
     "start_time": "2020-06-19T02:32:31.776576Z"
    }
   },
   "outputs": [],
   "source": [
    "security = security.div(security.iloc[0])\n",
    "candidates = candidates.div(candidates.iloc[0])\n",
    "spreads = candidates.sub(security, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:31.820181Z",
     "start_time": "2020-06-19T02:32:31.817131Z"
    }
   },
   "outputs": [],
   "source": [
    "n, m = spreads.shape\n",
    "X = np.ones(shape=(n, 2))\n",
    "X[:, 1] = np.arange(1, n+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heuristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:35.652908Z",
     "start_time": "2020-06-19T02:32:31.821252Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "470 µs ± 6.57 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np.linalg.inv(X.T @ X) @ X.T @ spreads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:45.183948Z",
     "start_time": "2020-06-19T02:32:35.654047Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.16 ms ± 9.41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "spreads.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:32:47.457982Z",
     "start_time": "2020-06-19T02:32:45.184886Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.8 ms ± 176 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "candidates.corrwith(security)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cointegration Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:36:07.415063Z",
     "start_time": "2020-06-19T02:34:18.075296Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.1 s ± 2.15 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "for candidate, prices in candidates.items():\n",
    "    df = pd.DataFrame({'s1': security,\n",
    "                       's2': prices})\n",
    "    var = VAR(df.values)\n",
    "    lags = var.select_order()\n",
    "    k_ar_diff = lags.selected_orders['aic']\n",
    "    coint_johansen(df, det_order=0, k_ar_diff=k_ar_diff)\n",
    "    coint(security, prices, trend='c')[:2]\n",
    "    coint(prices, security, trend='c')[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Heuristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T02:36:39.184978Z",
     "start_time": "2020-06-19T02:36:39.164884Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_pair_metrics(security, candidates):\n",
    "    security = security.div(security.iloc[0])\n",
    "    ticker = security.name\n",
    "    candidates = candidates.div(candidates.iloc[0])\n",
    "    spreads = candidates.sub(security, axis=0)\n",
    "    n, m = spreads.shape\n",
    "    X = np.ones(shape=(n, 2))\n",
    "    X[:, 1] = np.arange(1, n + 1)\n",
    "    drift = ((np.linalg.inv(X.T @ X) @ X.T @ spreads).iloc[1]\n",
    "             .to_frame('drift'))\n",
    "    vol = spreads.std().to_frame('vol')\n",
    "    corr_ret = (candidates.pct_change()\n",
    "                .corrwith(security.pct_change())\n",
    "                .to_frame('corr_ret'))\n",
    "    corr = candidates.corrwith(security).to_frame('corr')\n",
    "    metrics = drift.join(vol).join(corr).join(corr_ret).assign(n=n)\n",
    "    tests = []\n",
    "    for candidate, prices in candidates.items():\n",
    "        df = pd.DataFrame({'s1': security, 's2': prices})\n",
    "        var = VAR(df.values)\n",
    "        lags = var.select_order() # select VAR order\n",
    "        k_ar_diff = lags.selected_orders['aic']\n",
    "        # Johansen Test with constant Term and estd. lag order\n",
    "        cj0 = coint_johansen(df, det_order=0, k_ar_diff=k_ar_diff)\n",
    "        # Engle-Granger Tests\n",
    "        t1, p1 = coint(security, prices, trend='c')[:2]\n",
    "        t2, p2 = coint(prices, security, trend='c')[:2]\n",
    "        tests.append([ticker, candidate, t1, p1, t2, p2, \n",
    "                      k_ar_diff, *cj0.lr1])\n",
    "    columns = ['s1', 's2', 't1', 'p1', 't2', 'p2', 'k_ar_diff', 'trace0', 'trace1']\n",
    "    tests = pd.DataFrame(tests, columns=columns).set_index('s2')\n",
    "    return metrics.join(tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.589448Z",
     "start_time": "2020-06-19T02:36:39.571139Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 10 313.9\n",
      " 20 391.3\n",
      " 30 409.8\n",
      " 40 411.2\n",
      " 50 407.6\n",
      " 60 403.9\n",
      " 70 412.0\n",
      " 80 409.4\n",
      " 90 411.1\n",
      "100 422.4\n",
      "110 405.4\n",
      "120 406.2\n",
      "130 406.1\n"
     ]
    }
   ],
   "source": [
    "spreads = []\n",
    "start = 2010\n",
    "stop = 2019\n",
    "etf_candidates = etfs.loc[str(start): str(stop), :]\n",
    "stock_candidates = stocks.loc[str(start): str(stop), :]\n",
    "s = time()\n",
    "for i, (etf_ticker, etf_prices) in enumerate(etf_candidates.items(), 1):\n",
    "    df = compute_pair_metrics(etf_prices, stock_candidates)\n",
    "    spreads.append(df.set_index('s1', append=True))\n",
    "    if i % 10 == 0:\n",
    "        print(f'{i:>3} {time() - s:.1f}')\n",
    "        s = time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.703716Z",
     "start_time": "2020-06-19T04:09:40.592593Z"
    }
   },
   "outputs": [],
   "source": [
    "names = get_ticker_dict()\n",
    "spreads = pd.concat(spreads)\n",
    "spreads.index.names = ['s2', 's1']\n",
    "spreads = spreads.swaplevel()\n",
    "spreads['name1'] = spreads.index.get_level_values('s1').map(names)\n",
    "spreads['name2'] = spreads.index.get_level_values('s2').map(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.713319Z",
     "start_time": "2020-06-19T04:09:40.705017Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads['t'] = spreads[['t1', 't2']].min(axis=1)\n",
    "spreads['p'] = spreads[['p1', 'p2']].min(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engle-Granger vs Johansen: how do their findings compare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.722750Z",
     "start_time": "2020-06-19T04:09:40.715786Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads['trace_sig'] = ((spreads.trace0 > trace0_cv) &\n",
    "                        (spreads.trace1 > trace1_cv)).astype(int)\n",
    "spreads['eg_sig'] = (spreads.p < .05).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.742903Z",
     "start_time": "2020-06-19T04:09:40.723653Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>trace_sig</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg_sig</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21645</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1353</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "trace_sig      0    1\n",
       "eg_sig               \n",
       "0          21645  749\n",
       "1           1353  161"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(spreads.eg_sig, spreads.trace_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.746980Z",
     "start_time": "2020-06-19T04:09:40.743923Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads['coint'] = (spreads.trace_sig & spreads.eg_sig).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.763803Z",
     "start_time": "2020-06-19T04:09:40.748209Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 23908 entries, ('AAXJ.US', 'AA.US') to ('YCS.US', 'YUM.US')\n",
      "Data columns (total 19 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   drift      23908 non-null  float64\n",
      " 1   vol        23908 non-null  float64\n",
      " 2   corr       23908 non-null  float64\n",
      " 3   corr_ret   23908 non-null  float64\n",
      " 4   n          23908 non-null  int64  \n",
      " 5   t1         23908 non-null  float64\n",
      " 6   p1         23908 non-null  float64\n",
      " 7   t2         23908 non-null  float64\n",
      " 8   p2         23908 non-null  float64\n",
      " 9   k_ar_diff  23908 non-null  int64  \n",
      " 10  trace0     23908 non-null  float64\n",
      " 11  trace1     23908 non-null  float64\n",
      " 12  name1      23908 non-null  object \n",
      " 13  name2      23908 non-null  object \n",
      " 14  t          23908 non-null  float64\n",
      " 15  p          23908 non-null  float64\n",
      " 16  trace_sig  23908 non-null  int64  \n",
      " 17  eg_sig     23908 non-null  int64  \n",
      " 18  coint      23908 non-null  int64  \n",
      "dtypes: float64(12), int64(5), object(2)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "spreads.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.769970Z",
     "start_time": "2020-06-19T04:09:40.765849Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads = spreads.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.933403Z",
     "start_time": "2020-06-19T04:09:40.771185Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=np.log1p(spreads.t.abs()), \n",
    "                y=np.log1p(spreads.trace1), \n",
    "                hue='coint', data=spreads[spreads.trace0>trace0_cv]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.954133Z",
     "start_time": "2020-06-19T04:09:40.934343Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.to_hdf('heuristics.h5', 'spreads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.975476Z",
     "start_time": "2020-06-19T04:09:40.955177Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads = pd.read_hdf('heuristics.h5', 'spreads')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Heuristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:40.978702Z",
     "start_time": "2020-06-19T04:09:40.976455Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.drift = spreads.drift.abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:41.001203Z",
     "start_time": "2020-06-19T04:09:40.979570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>trace_sig</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg_sig</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21645</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1353</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "trace_sig      0    1\n",
       "eg_sig               \n",
       "0          21645  749\n",
       "1           1353  161"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(spreads.eg_sig, spreads.trace_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T04:09:41.017452Z",
     "start_time": "2020-06-19T04:09:41.002290Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>trace_sig</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg_sig</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90.53%</td>\n",
       "      <td>3.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.66%</td>\n",
       "      <td>0.67%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "trace_sig      0     1\n",
       "eg_sig                \n",
       "0         90.53% 3.13%\n",
       "1          5.66% 0.67%"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: f'{x:.2%}')\n",
    "pd.crosstab(spreads.eg_sig, spreads.trace_sig, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:51.580703Z",
     "start_time": "2020-06-19T10:22:51.219623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=4, figsize=(20, 5))\n",
    "for i, heuristic in enumerate(['drift', 'vol', 'corr', 'corr_ret']):\n",
    "    sns.boxplot(x='coint', y=heuristic, data=spreads, ax=axes[i])\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How well do the heuristics predict significant cointegration?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:54.875853Z",
     "start_time": "2020-06-19T10:22:54.844233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>coint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">corr</th>\n",
       "      <th>0</th>\n",
       "      <td>-38.88%</td>\n",
       "      <td>37.10%</td>\n",
       "      <td>77.53%</td>\n",
       "      <td>2374700.00%</td>\n",
       "      <td>99.17%</td>\n",
       "      <td>20.61%</td>\n",
       "      <td>-95.88%</td>\n",
       "      <td>60.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-49.28%</td>\n",
       "      <td>45.31%</td>\n",
       "      <td>81.63%</td>\n",
       "      <td>16100.00%</td>\n",
       "      <td>99.07%</td>\n",
       "      <td>20.13%</td>\n",
       "      <td>-93.50%</td>\n",
       "      <td>66.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">drift</th>\n",
       "      <th>0</th>\n",
       "      <td>0.04%</td>\n",
       "      <td>0.09%</td>\n",
       "      <td>0.17%</td>\n",
       "      <td>2374700.00%</td>\n",
       "      <td>1.92%</td>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.05%</td>\n",
       "      <td>0.10%</td>\n",
       "      <td>16100.00%</td>\n",
       "      <td>0.67%</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">vol</th>\n",
       "      <th>0</th>\n",
       "      <td>46.08%</td>\n",
       "      <td>80.12%</td>\n",
       "      <td>139.56%</td>\n",
       "      <td>2374700.00%</td>\n",
       "      <td>1560.03%</td>\n",
       "      <td>117.39%</td>\n",
       "      <td>5.77%</td>\n",
       "      <td>145.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.79%</td>\n",
       "      <td>51.40%</td>\n",
       "      <td>87.97%</td>\n",
       "      <td>16100.00%</td>\n",
       "      <td>561.25%</td>\n",
       "      <td>70.22%</td>\n",
       "      <td>8.10%</td>\n",
       "      <td>68.08%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                25%    50%     75%       count      max    mean     min  \\\n",
       "      coint                                                               \n",
       "corr  0     -38.88% 37.10%  77.53% 2374700.00%   99.17%  20.61% -95.88%   \n",
       "      1     -49.28% 45.31%  81.63%   16100.00%   99.07%  20.13% -93.50%   \n",
       "drift 0       0.04%  0.09%   0.17% 2374700.00%    1.92%   0.13%   0.00%   \n",
       "      1       0.01%  0.05%   0.10%   16100.00%    0.67%   0.07%   0.00%   \n",
       "vol   0      46.08% 80.12% 139.56% 2374700.00% 1560.03% 117.39%   5.77%   \n",
       "      1      24.79% 51.40%  87.97%   16100.00%  561.25%  70.22%   8.10%   \n",
       "\n",
       "                std  \n",
       "      coint          \n",
       "corr  0      60.70%  \n",
       "      1      66.96%  \n",
       "drift 0       0.18%  \n",
       "      1       0.08%  \n",
       "vol   0     145.64%  \n",
       "      1      68.08%  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spreads.groupby(spreads.coint)['drift', 'vol', 'corr'].describe().stack(level=0).swaplevel().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:55.032669Z",
     "start_time": "2020-06-19T10:22:55.019505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23747\n",
       "1      161\n",
       "Name: coint, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spreads.coint.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:55.279126Z",
     "start_time": "2020-06-19T10:22:55.272371Z"
    }
   },
   "outputs": [],
   "source": [
    "y = spreads.coint\n",
    "X = spreads[['drift', 'vol', 'corr', 'corr_ret']]\n",
    "# X = spreads[['drift', 'vol']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:57.248689Z",
     "start_time": "2020-06-19T10:22:57.240920Z"
    }
   },
   "outputs": [],
   "source": [
    "kf = StratifiedKFold(n_splits=5, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:57.407880Z",
     "start_time": "2020-06-19T10:22:57.402788Z"
    }
   },
   "outputs": [],
   "source": [
    "log_reg = LogisticRegressionCV(Cs=np.logspace(-10, 10, 21), \n",
    "                               class_weight='balanced',\n",
    "                               scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:59.019397Z",
     "start_time": "2020-06-19T10:22:57.503781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:-10.00, AUC: 64.75%'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_reg.fit(X=X, y=y)\n",
    "Cs = log_reg.Cs_\n",
    "scores = pd.DataFrame(log_reg.scores_[True], columns=Cs).mean()\n",
    "scores.plot(logx=True);\n",
    "f'C:{np.log10(scores.idxmax()):.2f}, AUC: {scores.max():.2%}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:59.023302Z",
     "start_time": "2020-06-19T10:22:59.020643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.51826270e-10, -2.81917597e-07, -2.87355303e-09,\n",
       "        -7.30577702e-08]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:59.086917Z",
     "start_time": "2020-06-19T10:22:59.024344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23747,     0],\n",
       "       [  161,     0]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = log_reg.predict_proba(X)[:, 1]\n",
    "confusion_matrix(y_true=spreads.coint, y_pred=(y_pred>.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:59.106818Z",
     "start_time": "2020-06-19T10:22:59.088268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2374700.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16100.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "      <td>50.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count   mean   std    min    25%    50%    75%    max\n",
       "coint                                                            \n",
       "0     2374700.00% 50.00% 0.00% 50.00% 50.00% 50.00% 50.00% 50.00%\n",
       "1       16100.00% 50.00% 0.00% 50.00% 50.00% 50.00% 50.00% 50.00%"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spreads.assign(y_pred=log_reg.predict_proba(X)[:, 1]).groupby(spreads.coint).y_pred.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:22:59.110443Z",
     "start_time": "2020-06-19T10:22:59.107891Z"
    }
   },
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(class_weight='balanced')\n",
    "decision_tree = GridSearchCV(model,\n",
    "                             param_grid={'max_depth': list(range(1, 10))},\n",
    "                             cv=5,\n",
    "                             scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:23:00.821455Z",
     "start_time": "2020-06-19T10:22:59.111601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(class_weight='balanced'),\n",
       "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:23:01.437510Z",
     "start_time": "2020-06-19T10:23:01.427814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'60.63%, Depth: 3'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{decision_tree.best_score_:.2%}, Depth: {decision_tree.best_params_[\"max_depth\"]}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T17:53:07.657747Z",
     "start_time": "2020-06-19T17:53:07.579741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(data=decision_tree.best_estimator_.feature_importances_, \n",
    "          index=X.columns).sort_values().plot.barh(title='Feature Importance')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:23:01.818332Z",
     "start_time": "2020-06-19T10:23:01.786502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2374700.00%</td>\n",
       "      <td>35.49%</td>\n",
       "      <td>17.34%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>31.45%</td>\n",
       "      <td>31.45%</td>\n",
       "      <td>31.45%</td>\n",
       "      <td>97.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16100.00%</td>\n",
       "      <td>64.51%</td>\n",
       "      <td>27.09%</td>\n",
       "      <td>31.45%</td>\n",
       "      <td>31.45%</td>\n",
       "      <td>68.79%</td>\n",
       "      <td>86.26%</td>\n",
       "      <td>97.15%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count   mean    std    min    25%    50%    75%    max\n",
       "coint                                                             \n",
       "0     2374700.00% 35.49% 17.34%  0.00% 31.45% 31.45% 31.45% 97.15%\n",
       "1       16100.00% 64.51% 27.09% 31.45% 31.45% 68.79% 86.26% 97.15%"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spreads.assign(y_pred=decision_tree.predict_proba(X)[:, 1]).groupby(spreads.coint).y_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T10:23:02.091858Z",
     "start_time": "2020-06-19T10:23:01.964910Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='coint', \n",
    "            y='y_pred', \n",
    "            data=spreads.assign(y_pred=decision_tree.predict_proba(X)[:, 1]), \n",
    "            kind='box');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "309px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
